Connectionists: [CFP] Special Issue on Human-in-the-loop Machine Learning and its Applications - NCAA Journal

[Joni Zhong]

https://www.springer.com/journal/521/updates/17925472

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Call for Papers: NCAA Special Issue on human-in-the-loop machine learning and its applications
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Deadline for submission: Dec 31, 2020
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Neural Computing and Applications | Topical collection on human-in-the-loop machine learning and its applications - Springer<https://www.springer.com/journal/521/updates/17925472>
Topical Collection on Human-in-the-loop Machine Learning and its Applications. Aims, Scope and Objective. Human-in-the-Loop (HIL) means including human feedback into ...
www.springer.com


Dear Colleagues,

Human-in-the-Loop (HIL) means including human feedback into the training loop of the machine learning models in order to facilitate the following requirements: 1) to improve the quality of training and reduce/prevent the error of the model. When the testing error is larger than a certain threshold, the HIL learning model is able to obtain the new data-points from the users in an interactive way. In some situations, a large error produced by the model should be avoided. For instance, reinforcement learning alone is not sufficient to achieve safety if there exists an exploration policy in robot manipulation, by which some unexpected actions may be generated. In such scenarios, the data-points from human guidance are crucial during both robot’s safe execution as well as model optimization.  2) to incorporate the human user labelling to improve the pre-trained models.  During the training of the state-of-the-art models, the quality of the training data-sets is extremely important. One solution to actively incorporate more data is optimizing the models by including the human users’ feedback (e.g. rewards in RL) or new data points (e.g. supervised learning) to adapt the pre-trained models in different environments.

This special issue will also offer the opportunity for researchers and practitioners in the diverse fields of robotics to showcase its solutions and applications where human reinforcement feedback would have a positive impact on the training processes.  The inclusion of HIL would allow robots and machine learning models to use both internal and external feedback to speed up the learning process and also improve its performance.  In many ways this could allow the models to learn through their own self-reflection as well as the external input from a human.

Specifically, as a following-up journal publication of the special session in HIL machine learning in IEEE SMC 2020, the extended versions of the accepted paper are mostly welcomed.

Topics of interest include, but are not limited to:

Human Guided Reinforcement Learning
Human-robot Collaboration
Human-robot Social Interaction
Dialogue Systems with Human-in-the-loop
Interpretable Machine Learning with Human-in-the-loop
Active Learning and Continuous Learning
Learning by Demonstration
Human Factors in HCI/HRI
etc.

Deadlines

Deadline for submissions: 31st December 2020
Deadline for review:          28th February 2021
Decisions:                          20th March 2021
Deadline for revised version by authors: 20th April 2021
Deadline for 2nd review:    10th May 2021
Final decisions:                  20th May 2021

Guest Editors

Dr. Joni Zhong (Lead Guest Editor), The Hong Kong Polytechnic University, Hong Kong, joni.zhong at ieee.org
Dr. Mark Elshaw, Coventry University, UK, mark.elshaw at coventry.ac.uk
Dr. Yanan Li, Sussex University, UK, yl557 at sussex.ac.uk
Prof. Dr. Stefan Wermter, University of Hamburg, Germany, wermter at informatik.uni-hamburg.de
Prof. Xiaofeng Liu, Hohai University, China, xfliu at hhu.edu.cn



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